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11,200 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Information Flows in Causal Networks
This notebook replicates some examples from Ay & Polani (2008), "Information Flows in Causal Networks" in Advances in Complex Systems Volume 11, Issue 01... | Python Code:
from causalinfo import *
from numpy import log2
from numpy.testing import assert_allclose
# You only need this if you want to draw pretty pictures of the Networksa
from nxpd import draw, nxpdParams
nxpdParams['show'] = 'ipynb'
w, x, y, z = make_variables("W X Y Z", 2)
wdist = UniformDist(w)
Explanation: In... |
11,201 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This IPython notebook illustrates how to select the best learning based matcher. First, we need to import py_entitymatching package and other libraries as follows
Step1: Then, ... | Python Code:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Set the seed value
seed = 0
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'dblp_demo.csv'
path_B = datasets_dir + os.sep + 'acm_demo.... |
11,202 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting Models Exercise 1
Imports
Step1: Fitting a quadratic curve
For this problem we are going to work with the following model
Step2: First, generate a dataset using this model using th... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Fitting Models Exercise 1
Imports
End of explanation
a_true = 0.5
b_true = 2.0
c_true = -4.0
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following model:... |
11,203 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Usuarios de Energía Eléctrica
Parámetros que se obtienen desde esta fuente
ID |Descripción
---|
Step1: 2. Descarga de datos
Step2: 3. Estandarizacion de datos de Parámetros
Step3: Exporta... | Python Code:
descripciones = {
'P0609': 'Usuarios Electricos'
}
# Librerias utilizadas
import pandas as pd
import sys
import urllib
import os
import csv
import zipfile
# Configuracion del sistema
print('Python {} on {}'.format(sys.version, sys.platform))
print('Pandas version: {}'.format(pd.__version__))
import pla... |
11,204 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Observations
Observations can be thought of as the probability of being in any given state at each time step. For this demonstration, observations are randomly initialized. In a real case, t... | Python Code:
observations = np.random.random((1, 90, 2)) * 4 - 2
plot(observations[0,:,:])
grid()
observations_variable = tf.Variable(observations)
posterior_graph, _, _ = hmm_tf.forward_backward(tf.sigmoid(observations_variable))
# build error function
sum_error_squared = tf.reduce_sum(tf.square(truth - posterior_grap... |
11,205 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 9
Step1: In this example, we switched the ordering of the arguments between the two function calls; consequently, the ordering of the arguments inside the function were also flipped... | Python Code:
def pet_names(name1, name2):
print("Pet 1: {}".format(name1))
print("Pet 2: {}".format(name2))
pet1 = "King"
pet2 = "Reginald"
pet_names(pet1, pet2)
pet_names(pet2, pet1)
Explanation: Lecture 9: Functions II
CSCI 1360: Foundations for Informatics and Analytics
Overview and Objectives
In the previou... |
11,206 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell
Step1: Basic rich display
Find a Physics related image on the internet and display it in t... | Python Code:
# YOUR CODE HERE
from IPython.display import display, Image
assert True # leave this to grade the import statements
Explanation: Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell:
End of explanation
# YOUR CODE HERE
Image(url='http://www.redorbit.com/media/u... |
11,207 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load data
Let's load our data to analyze. For this example, I'm going to use some stock market data to be able to show some clear trend changes. This data can be downloaded from FRED (https
... | Python Code:
market_df = pd.read_csv('../examples/SP500.csv', index_col='DATE', parse_dates=True)
market_df.head()
Explanation: Load data
Let's load our data to analyze. For this example, I'm going to use some stock market data to be able to show some clear trend changes. This data can be downloaded from FRED (https://... |
11,208 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a pipeline
Step1: Finding the best model | Python Code:
%pylab inline
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_digits
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
digits = load_digits()
X_digits = digits.data
y_digits = digits.target
logistic = LogisticRegression()
pca = ... |
11,209 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plasma comparison
Step1: The example tardis_example can be downloaded here
tardis_example.yml
Step2: Accessing the plasma states
In this example, we are accessing Si and also the unionized... | Python Code:
from tardis.simulation import Simulation
from tardis.io.config_reader import Configuration
from IPython.display import FileLinks
Explanation: Plasma comparison
End of explanation
config = Configuration.from_yaml('tardis_example.yml')
sim = Simulation.from_config(config)
Explanation: The example tardis_exam... |
11,210 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.... | Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network ... |
11,211 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LRG Position Matching
We need a sample of LRGs that span the range of redshifts and i-band magnitudes found in the OM10 catalog. The sample can be kept small by selecting in color as well as... | Python Code:
%matplotlib inline
import om10,os
import numpy as np
import matplotlib.pyplot as plt
import triangle
Explanation: LRG Position Matching
We need a sample of LRGs that span the range of redshifts and i-band magnitudes found in the OM10 catalog. The sample can be kept small by selecting in color as well as ma... |
11,212 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Python Packages for Science
The Aeropython’s guide to the Python Galaxy!
Siro Moreno Martín
Alejandro Sáez Mollejo
0. Introduction
Python in the Scientific environment
Principal Python... | Python Code:
from IPython.display import HTML
HTML('<iframe src="http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf" width="700" height="400"></iframe>')
Explanation: Basic Python Packages for Science
The Aeropython’s guide to the Python Galaxy!
Siro Moreno Martín
Alejandro Sáez Mollejo
0. Introduction
Python... |
11,213 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Assembly of system with multiple domains, variables and numerics
This tutorial demonstrates how a transient problem may be solved in PorePy.
We consider the advective-diffusive tracer trans... | Python Code:
import numpy as np
import scipy.sparse as sps
import porepy as pp
import data.flow_benchmark_2d_geiger_setup as setup
Explanation: Assembly of system with multiple domains, variables and numerics
This tutorial demonstrates how a transient problem may be solved in PorePy.
We consider the advective-diffusiv... |
11,214 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inference Sandbox
In this notebook, we'll mock up some data from the linear model, as reviewed here. Then it's your job to implement a Metropolis sampler and constrain the posterior distriub... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 5.0)
# the model parameters
a = np.pi
b = 1.6818
# my arbitrary constants
mu_x = np.exp(1.0) # see definitions above
tau_x = 1.0
s = 1.0
N = 50 # number of data points
# get some... |
11,215 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Note
Step1: Soluition
Step2: Bonus | Python Code:
first_commit = git_log.index[-1]
first_commit
today = pd.to_datetime('today')
type(today)
Explanation: Note: We are using the UNIX timestamp here because it's superfast to convert it to a real datatime64 data type.
Cleaning up wrong timestamps
_Note: 'today'is suboptimal
End of explanation
git_log[(git_log... |
11,216 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
==============================================
Read and visualize projections (SSP and other)
==============================================
This example shows how to read and visualize Sign... | Python Code:
# Author: Joan Massich <mailsik@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import read_proj
from mne.io import read_raw_fif
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname = data_path ... |
11,217 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Let's start off with a square figure.
Step1: Give it a nice wooden color http
Step2: The default background color for Axes is white. Let's propagate the figure background color by making t... | Python Code:
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(1, 1, 1)
plt.show()
Explanation: Let's start off with a square figure.
End of explanation
BOARD_COLOR = '#d7be9f'
fig = plt.figure(figsize=(5, 5), facecolor=BOARD_COLOR)
ax = fig.add_subplot(1, 1, 1)
plt.show()
Explanation: Give it a nice wooden color h... |
11,218 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dropout
Dropout是一种用于深度神经网络的方法,用于避免过拟合.
在训练时,每次迭代中每层以提前设定的概率,称为keep_prob,来随机选择保留的节点,其他的节点在该次前向传播被忽略(设为0),同时在后向传播中也忽略.如下图中打叉的节点就是某次迭代中忽略的节点.
作用,应用
Dropout主要有两个作用
Step2: 用pytorch来实现,尽量做到每层保留... | Python Code:
keep_prob = 0.5
do_dropout = True
Explanation: Dropout
Dropout是一种用于深度神经网络的方法,用于避免过拟合.
在训练时,每次迭代中每层以提前设定的概率,称为keep_prob,来随机选择保留的节点,其他的节点在该次前向传播被忽略(设为0),同时在后向传播中也忽略.如下图中打叉的节点就是某次迭代中忽略的节点.
作用,应用
Dropout主要有两个作用:
dropout在训练时每个迭代中相当于减小了网络规模,有正则化的作用.
dropout可以避免把过多的权重放在某个节点上,而是把权重分散给全部的节点.
在实践中,dropout在C... |
11,219 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This script shows how to use the existing code in opengrid to create a baseload electricity consumption benchmark.
Step1: Script settings
Step2: We create one big dataframe, the columns ar... | Python Code:
import os, sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# add the path to opengrid to sys.path
sys.path.append(os.path.join(script_dir, os.pardir, os.... |
11,220 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sinusoid autoencoder trained with multiple phases
Let's provide more training examples - sinusoid with various phases.
Step1: The model should be able to handle noise-corrupted input signal... | Python Code:
%pylab inline
import keras
import numpy as np
import keras
N = 50
# phase_step = 1 / (2 * np.pi)
t = np.arange(50)
phases = np.linspace(0, 1, N) * 2 * np.pi
x = np.array([np.sin(2 * np.pi / N * t + phi) for phi in phases])
print(x.shape)
imshow(x);
plot(x[0]);
plot(x[1]);
plot(x[2]);
from keras.models impo... |
11,221 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1 Introduccion a IPython notebooks/ Jupyter
Que es exactamente?
Una libreta IPython/Jupyter es un ambiente interactivo para escribir y correr codigo de python. Es un historial completo y aut... | Python Code:
print("hola bolivia")
Explanation: 1 Introduccion a IPython notebooks/ Jupyter
Que es exactamente?
Una libreta IPython/Jupyter es un ambiente interactivo para escribir y correr codigo de python. Es un historial completo y auto-contenido de un calculo y puede ser convertido a otros formatos para compartir c... |
11,222 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Try not to peek at the solutions when you go through the exercises. ;-)
First let's make sure this notebook works well in both Python 2 and Python 3
Step1: TensorFlow basics
Step2: Constru... | Python Code:
from __future__ import absolute_import, division, print_function, unicode_literals
Explanation: Try not to peek at the solutions when you go through the exercises. ;-)
First let's make sure this notebook works well in both Python 2 and Python 3:
End of explanation
import tensorflow as tf
tf.__version__
Exp... |
11,223 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Autoregressive (AR) Models
by Maxwell Margenot, Delaney Mackenzie, and Lee Tobey
Lee Tobey is the founder of Hedgewise.
Part of the Quantopian Lecture Series
Step1: Note how this process fl... | Python Code:
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import statsmodels.tsa as tsa
import matplotlib.pyplot as plt
# ensures experiment runs the same every time
np.random.seed(100)
# This function simluates an AR process, generating a new value based on historial valu... |
11,224 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Github
https
Step3: List Comprehensions
Step4: Dictionaries
Python dictionaries are awesome. They are hash tables and have a lot of neat CS properties. Learn and use them well. | Python Code:
# Create a [list]
days = ['Monday', # multiple lines
'Tuesday', # acceptable
'Wednesday',
'Thursday',
'Friday',
'Saturday',
'Sunday', # trailing comma is fine!
]
days
# Simple for-loop
for day in days:
print(day)
# Double for-loop
for day in da... |
11,225 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of indiv... | Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dim... |
11,226 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow Serving in 10 minutes!
TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, ev... | Python Code:
import tensorflow as tf
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x,W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logit... |
11,227 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Factory methods
Factory methods are the primary way of creating useful geometries fast. They form an abstraction level up from knot vectors and control-points to give a cleaner simpler inter... | Python Code:
import splipy as sp
import numpy as np
import splipy.curve_factory as curve_factory
import splipy.surface_factory as surface_factory
import splipy.volume_factory as volume_factory
Explanation: Factory methods
Factory methods are the primary way of creating useful geometries fast. They form an abstractio... |
11,228 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
You are currently looking at version 1.1 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook ... | Python Code:
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
# print(cancer.DESCR)
Explanation: You are currently looking at version 1.1 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera p... |
11,229 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimization Exercise 1
Imports
Step1: Hat potential
The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the "hat potential... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Optimization Exercise 1
Imports
End of explanation
def hat(x,a,b):
v = -a*(x**2) + b*(x**4)
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.... |
11,230 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Abstract
Neural networking is a form of machine learning inspired by the biological function of the human brain. By giving the program known inputs and outputs of a function, the computer le... | Python Code:
import numpy as np
from sklearn.datasets import load_digits
digits = load_digits()
from IPython.html.widgets import interact
%matplotlib inline
import matplotlib.pyplot as plt
import timeit
from IPython.display import Image
import NNpix as npx
import timeit
Explanation: Abstract
Neural networking is a for... |
11,231 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
Step1: Load some data
I'm going to work with the data from the combined data sets. The analysis for this data set is in analysis\Cf072115_to_Cf072215b.
The one limitation here is that ... | Python Code:
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import imageio
import pandas as pd
import seaborn as sns
sns.set(style='ticks')
sys.path.append('../scripts/')
import bicorr as bicorr
import bicorr_e as bicorr_e
import bicorr_plot as bicorr_plot
import bicorr_sums as bicorr_sums
impo... |
11,232 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Air-Standard Brayton Cycle Example
Imports
Step1: Definitions
Step2: Problem Statement
An ideal air-standard Brayton cycle operates at steady state with compressor inlet conditions of 300.... | Python Code:
from thermostate import State, Q_, units
from thermostate.plotting import IdealGas
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Air-Standard Brayton Cycle Example
Imports
End of explanation
substance = 'air'
p_1 = Q_(1.0, 'bar')
T_1 = Q_(300.0, 'K')
T_3 = Q_(1700.0, 'K... |
11,233 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-3', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Ti... |
11,234 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-1', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glaciers, Ice.
Proper... |
11,235 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neighborhood Structures in the ArcGIS Spatial Statistics Library
Spatial Weights Matrix
On-the-fly Neighborhood Iterators [GA Table]
Contructing PySAL Spatial Weights
Spatial Weight Matrix ... | Python Code:
import Weights as WEIGHTS
import os as OS
inputFC = r'../data/CA_Polygons.shp'
fullFC = OS.path.abspath(inputFC)
fullPath, fcName = OS.path.split(fullFC)
masterField = "MYID"
Explanation: Neighborhood Structures in the ArcGIS Spatial Statistics Library
Spatial Weights Matrix
On-the-fly Neighborhood Iterato... |
11,236 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then ... | Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(fl... |
11,237 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow's Deep MNIST tutorial
https
Step1: Initiate a tf.session
We're going to eventually define a graph which will represent a "dataflow computation". Before we start buiding our graph... | Python Code:
#load mnist data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
Explanation: TensorFlow's Deep MNIST tutorial
https://www.tensorflow.org/get_started/mnist/pros
start tf.session
define a model
define a training loss function
train usi... |
11,238 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import libraries
Step1: Define source paths
Step2: Import data
Step3: Data exploration
Shape, types
Step4: Missing values
Step5: We want to know what you look for in the opposite sex.
S... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
%matplotlib inline
Explanation: Import libraries
End of explanation
source_path = "/Users/sandrapietrowska/Documents/Trainings/luigi/data_source/"
Explanation: Define source paths
End of explan... |
11,239 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example to_hdf calls
Initialize the simulation with the tardis_example.yml configuration file.
Step1: Run the simulation while storing all its iterations to an HDF file.
The first parameter... | Python Code:
from tardis.io.config_reader import Configuration
from tardis.model import Radial1DModel
from tardis.simulation import Simulation
# Must have the tardis_example folder in the working directory.
config_fname = 'tardis_example/tardis_example.yml'
tardis_config = Configuration.from_yaml(config_fname)
model = ... |
11,240 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Preparations" data-toc-modified-id="Preparations-1"><span class="toc-item-num">1 </span>Preparations</a></div><div class=... | Python Code:
bigsourcefile = 'TextProcessing_2017/W0013.orig.txt' # This is the path to our file
input = open(bigsourcefile, encoding='utf-8').readlines() # We use a variable 'input' for
# keeping its contents.
input[:10] ... |
11,241 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was prepared by wdonahoe. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem
Step1: Unit Test
The following unit test is expected to fail u... | Python Code:
def group_ordered(list_in):
# TODO: Implement me
pass
Explanation: <small><i>This notebook was prepared by wdonahoe. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement a function that groups identical items based on their order in the list.
Constraints
Test Case... |
11,242 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Tour of SciKit-Learn + TensorFlow + SkFlow
When we talk about Data Science and the Data Science Pipeline, we are typically talking about the management of data flows for a specific purpose... | Python Code:
%matplotlib inline
# Things we'll need later
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.metrics import classification_report
from sklearn import cross_validation as cv
# Load the exam... |
11,243 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Styling
New in version 0.17.1
<span style="color
Step1: Here's a boring example of rendering a DataFrame, without any (visible) styles
Step2: Note
Step4: The row0_col2 is the identifier f... | Python Code:
import matplotlib.pyplot
# We have this here to trigger matplotlib's font cache stuff.
# This cell is hidden from the output
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE... |
11,244 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Data
Step2: View All Rows
Step3: View Rows Where Age Is Greater Than 20 And City Is San Francisco
Step4: View Rows Where Age Is Greater Than 20 or City Is San Francis... | Python Code:
# Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
Explanation: Title: Multiple Conditional Statements
Slug: multiple_conditional_statements
Summary: Multiple Conditional Statements in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial w... |
11,245 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
目录遍历
下面整理常见的目录遍历方式
Step1: os.scandir
返回可迭代的对象os.DirEntry ,可以直接判断是文件还是目录等.
Step2: os.walk
可以递归遍历目录, 另外还支持遍历符号链接指向的目录.
Step3: glob.glob
如果文件比较多的话可以使用glob.iglob提高性能.
Step4: pathlib.Path
自从p... | Python Code:
import os
os.listdir('traverse-directories')
Explanation: 目录遍历
下面整理常见的目录遍历方式:
os.listdir
os.scandir
os.walk
os.listdir
glob.glob
pathlib.Path
目录结构为:
```
traverse-directories
sell.txt
fuit-shop\
orange.txt
apple.txt
car\
small-car\
奔驰.txt
big-car\
... |
11,246 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Async optimization Loop
Bayesian optimization is used to tune parameters for walking robots or other
experiments that are not a simple (expensive) function call.
Tim Head, February 2017.
Ref... | Python Code:
print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
Explanation: Async optimization Loop
Bayesian optimization is used to tune parameters for walking robots or other
experiments that are not a simple (expensive) function call.
Tim Head, February 2017.
Reformatted by Holge... |
11,247 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Outline
Glossary
2. Mathematical Groundwork
Previous
Step1: Import section specific modules | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: Outline
Glossary
2. Mathematical Groundwork
Previous: 2.5 Convolution
Next: 2.7 Fourier Theorems
Import standard modules:
End of explanation
pa... |
11,248 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Imports and configuration
We set the path to the config.cfg file using the environment variable 'PYPMJ_CONFIG_FILE'. If you do not have a configuration file yet, please look into the Setting... | Python Code:
import os
os.environ['PYPMJ_CONFIG_FILE'] = '/path/to/your/config.cfg'
Explanation: Imports and configuration
We set the path to the config.cfg file using the environment variable 'PYPMJ_CONFIG_FILE'. If you do not have a configuration file yet, please look into the Setting up a configuration file example.... |
11,249 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 2
Imports
Step2: Factorial
Write a function that computes the factorial of small numbers using np.arange and np.cumprod.
Step4: Write a function that computes the factorial ... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 2
Imports
End of explanation
def np_fact(n):
Compute n! = n*(n-1)*...*1 using Numpy.
if n==0:
return 1
else:
a=np.arange(1.0, (n+1), 1.0)
b= a.cumprod(... |
11,250 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Helpers
Step3: Load exekall's ValueDB
This notebook is meant for analysing a set of results coming from a test session executed using exekall (potentially via bisector).
Since it onl... | Python Code:
def collect_value(db, cls, key_path):
Collect objects computed for the exekall subexpression
pointed at by ``key_path``, starting from objects of type ``cls``.
The path is a list of parameter names that allows locating a node in the graph of an expression.
The path from z to x is ['par... |
11,251 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
!!! D . R . A . F . T !!!
Lightness
Lightness is defined as the brightness of an area judged relative to the brightness of a similarly illuminated area that appears to be white or highly tra... | Python Code:
import colour
colour.utilities.filter_warnings(True, False)
sorted(colour.LIGHTNESS_METHODS.keys())
Explanation: !!! D . R . A . F . T !!!
Lightness
Lightness is defined as the brightness of an area judged relative to the brightness of a similarly illuminated area that appears to be white or highly transmi... |
11,252 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analysis tools from scipy and other python libra... | Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
sim.units = ('AU', 'yr', 'Msun')
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
Explanation: Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analys... |
11,253 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Occasionally, absolutely crazy ideas crop up into my noggin. Recently, I've had two take up residence almost simultaneously, both related to pynads.
Haskell Type Signatures
Since Pynads is, ... | Python Code:
def my_func(a: int, b: str = 'hello') -> tuple:
return (a, b)
my_func(1, 'wut')
Explanation: Occasionally, absolutely crazy ideas crop up into my noggin. Recently, I've had two take up residence almost simultaneously, both related to pynads.
Haskell Type Signatures
Since Pynads is, nominally, a learnin... |
11,254 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#ATENÇÃO
Step1: Note que a imagem possui 174 linhas e 314 colunas, totalizando mais de 54 mil pixels. A representação do pixel é pel... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
!ls ../data
f = mpimg.imread('../data/cameraman.tif')
print('Tamanho de f: ', f.shape)
print('Tipo do pixel:', f.dtype)
print('Número total de pixels:', f.size)
print('Pixels:\n', f)
Explanation: Table of... |
11,255 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have an array : | Problem:
import numpy as np
a = np.array([[ 0, 1, 2, 3, 5, 6, 7, 8],
[ 4, 5, 6, 7, 5, 3, 2, 5],
[ 8, 9, 10, 11, 4, 5, 3, 5]])
low = 1
high = 5
result = a[:, low:high] |
11,256 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute sparse inverse solution with mixed norm
Step1: Run solver with SURE criterion
Step2: Plot dipole activations
Step3: Plot residual
Step4: Generate stc from dipoles
Step5: View i... | Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm, make_stc_from_dipoles
from mne.minimum_norm import make... |
11,257 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wayne Nixalo - 4 Jun 2017
Codealong of Practical Deep Learning I Lesson 4 statefarm JNB. My comments are in italics.
6 Jun 2017 NOTE
Step1: Setup Batches
Step2: Rather than using batches, ... | Python Code:
import theano
import os, sys
sys.path.insert(1, os.path.join('utils'))
%matplotlib inline
from __future__ import print_function, division
path = "data/statefarm/"
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
# batch_size=32
batch_size=16
Explanation: Wayne Nixalo - 4... |
11,258 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TFRecord and tf.Example
Learning Objectives
Understand the TFRecord format for storing data
Understand the tf.Example message type
Read and Write a TFRecord file
Introduction
In this noteboo... | Python Code:
# Run the chown command to change the ownership of the repository
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searche... |
11,259 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Morph volumetric source estimate
This example demonstrates how to morph an individual subject's
Step1: Setup paths
Step2: Compute example data. For reference see ex-inverse-volume.
Load da... | Python Code:
# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import nibabel as nib
import mne
from mne.datasets import sample, fetch_fsaverage
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
Explanat... |
11,260 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This notebook shows how to plot an XRD plot for the two polymorphs of CsCl ($Pm\overline{3}m$ and $Fm\overline{3}m$). You can also use matgenie.py's diffraction command to plot ... | Python Code:
# Set up some imports that we will need
from pymatgen import Lattice, Structure
from pymatgen.analysis.diffraction.xrd import XRDCalculator
from IPython.display import Image, display
%matplotlib inline
Explanation: Introduction
This notebook shows how to plot an XRD plot for the two polymorphs of CsCl ($Pm... |
11,261 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualization in Depth With Bokeh
For details on bokeh, see http
Step1: Objectives
Describe how to create interactive visualizations using bokeh
Running example and visualization goals
How ... | Python Code:
from bokeh.plotting import figure, output_file, show, output_notebook, vplot
import random
import numpy as np
import pandas as pd
output_notebook() # Use so see output in the Jupyter notebook
import bokeh
bokeh.__version__
Explanation: Visualization in Depth With Bokeh
For details on bokeh, see http://bok... |
11,262 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GRID_LRT Testbed Notebook
1. Setting up the Environment
The GRID LOFAR TOOLS have several infrastructure requirements. They are as follows
Step1: This should give a confirmation of that you... | Python Code:
import os
import GRID_LRT
print(GRID_LRT.__file__)
import subprocess
from GRID_LRT.get_picas_credentials import picas_cred
from GRID_LRT.Staging import stage_all_LTA
from GRID_LRT.Staging import state_all
from GRID_LRT.Staging import stager_access
from GRID_LRT.Staging.srmlist import srmlist
from GRID_LRT ... |
11,263 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Driving Car Engineer Nanodegree
Deep Learning
Project
Step1: Step 1
Step2: Include an exploratory visualization of the dataset
Visualize the German Traffic Signs Dataset using the pic... | Python Code:
# Load pickled data
import pickle
import cv2 # for grayscale and normalize
# TODO: Fill this in based on where you saved the training and testing data
training_file ='traffic-signs-data/train.p'
validation_file='traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
with open(training_file,... |
11,264 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting the cloudsat groundtrack on a modis raster
This notebook is my solution to Assignment 16, satellite groundtrack assigned on Day 26
Environment requires
Step1: 1. Read in the ground... | Python Code:
from a301utils.a301_readfile import download
from a301lib.cloudsat import get_geo
import glob
import os
from pathlib import Path
import sys
import json
import numpy as np
import h5py
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap
rad_file='MYD021KM.A2006303.2220.006.201207814... |
11,265 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-ll', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-LL
Sub-Topics: Radiative Forcings.
... |
11,266 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p><font size="6"><b> Case study
Step1: Some of the data files that are available from AirBase were included in the data folder
Step2: Processing a single file
We will start with processin... | Python Code:
from IPython.display import HTML
HTML('<iframe src=http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8#tab-data-by-country width=900 height=350></iframe>')
Explanation: <p><font size="6"><b> Case study: air quality data of European monitoring stations (AirBase)</b></font... |
11,267 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten d... | Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
Explanation: Generative Adversarial Network
In this notebook, we'll be building a gen... |
11,268 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using LAMMPS with iPython and Jupyter
LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up.
Installation
Download the latest version of LAMMPS into a ... | Python Code:
from lammps import IPyLammps
L = IPyLammps()
# 2d circle of particles inside a box with LJ walls
import math
b = 0
x = 50
y = 20
d = 20
# careful not to slam into wall too hard
v = 0.3
w = 0.08
L.units("lj")
L.dimension(2)
L.atom_style("bond")
L.boundary("f f p")
L.lattice("hex", 0.85)
L.r... |
11,269 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook covers using metrics to analyze the 'accuracy' of prophet models. In this notebook, we will extend the previous example (http
Step1: Read in the data
Read the data in from the... | Python Code:
import pandas as pd
import numpy as np
from fbprophet import Prophet
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
%matplotlib inline
plt.rcParams['figure.figsize']=(20,10)
plt.style.use('ggplot')
Explanation: This notebook covers using metr... |
11,270 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training and Inference Module
We modularized commonly used codes for training and inference in the module (or mod for short) package. This package provides intermediate-level and high-level ... | Python Code:
import mxnet as mx
from data_iter import SyntheticData
# mlp
net = mx.sym.Variable('data')
net = mx.sym.FullyConnected(net, name='fc1', num_hidden=64)
net = mx.sym.Activation(net, name='relu1', act_type="relu")
net = mx.sym.FullyConnected(net, name='fc2', num_hidden=10)
net = mx.sym.SoftmaxOutput(net, name... |
11,271 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Source of the materials
Step1: The 'catch' is that you have to work with SeqRecord objects (see Chapter 4), which contain a Seq object (Chapter 3) plus annotation like an identifier and des... | Python Code:
from Bio import SeqIO
help(SeqIO)
Explanation: Source of the materials: Biopython Tutorial and Cookbook (adapted)
Sequence Input/Output
In this notebook we'll discuss in more detail the Bio.SeqIO module, which was briefly introduced before. This aims to provide a simple interface for working with assorted ... |
11,272 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2.0. Co-author Graphs
Social network analysis is a popular method in the field of historical network research. By far the most accessible source of information about social networks in scien... | Python Code:
from tethne.readers import wos
metadata = wos.read('../data/Baldwin/PlantPhysiology',
streaming=True, index_fields=['date'], index_features=['authors'])
Explanation: 2.0. Co-author Graphs
Social network analysis is a popular method in the field of historical network research. By far th... |
11,273 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Data
We use the "Howell" dataset, which consists of measurements of height, weight, age and sex, of a certain foraging tribe, collected by Nancy Howell.
Step2: Empiri... | Python Code:
import numpy as np
np.set_printoptions(precision=3)
import matplotlib.pyplot as plt
import math
import os
import warnings
import pandas as pd
# from scipy.interpolate import BSpline
# from scipy.stats import gaussian_kde
!mkdir figures
!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro
import ... |
11,274 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graph Construction and feature engineering
Load library
Step1: Load data
Step2: Graph generation & analysis
Build proper edge array
Step3: Generate a MultiDigraph with networkx and edge a... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import networkx as nx
import pygraphviz as pgv
import pydot as pyd
from networkx.drawing.nx_agraph import graphviz_layout
from networkx.drawing.nx_agraph import write_dot
Explanation: Graph Construction and feature en... |
11,275 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Before you leave, the Elves in accounting just need you to fix your expense report (your puzzle input); apparently, something isn't quite adding up.
Specifically, they need you to find the t... | Python Code:
for a,b in itertools.permutations(list(map(int, data)), 2):
if a+b == 2020:
print(a*b)
break
Explanation: Before you leave, the Elves in accounting just need you to fix your expense report (your puzzle input); apparently, something isn't quite adding up.
Specifically, they need you to f... |
11,276 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
k-Nearest Neighbor (kNN) exercise
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For ... | Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsi... |
11,277 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ants in Space! An introduction to the code in beam_paco__gtoc5
Luís F. Simões
2017-04
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
Step1: Taking a look at our Python envi... | Python Code:
# https://esa.github.io/pykep/
# https://github.com/esa/pykep
# https://pypi.python.org/pypi/pykep/
import PyKEP as pk
import numpy as np
from tqdm import tqdm, trange
import matplotlib.pylab as plt
%matplotlib inline
import seaborn as sns
plt.rcParams['figure.figsize'] = 10, 8
from gtoc5 import *
from gto... |
11,278 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook downloads and cleans the SCOTUS subnetwork data. It can be modified to create any jurisdiction subnetwork and also the federal appelate subnetwork.
You have to modify the two p... | Python Code:
# modify these for your own computer
repo_directory = '/Users/iaincarmichael/Dropbox/Research/law/law-net/'
data_dir = '/Users/iaincarmichael/data/courtlistener/'
Explanation: This notebook downloads and cleans the SCOTUS subnetwork data. It can be modified to create any jurisdiction subnetwork and also th... |
11,279 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Car Evaluation using Decision trees and Random Forests
<hr>
Decision tree learning
Decision tree classifiers are attractive models of Machine Learning as they emphasize on interpretability.
... | Python Code:
import os
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
from time import time
from sklearn import... |
11,280 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create some text
Step2: Apply regex | Python Code:
# Load regex package
import re
Explanation: Title: Match A Word
Slug: match_a_word
Summary: Match A Word
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
# Create a variable containing a text string
text = 'Th... |
11,281 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
COVID19 Exposure Notification System Risk Simulator
kpmurphy@google.com, serghiou@google.com
(broken link)
Last update
Step1: Infectiousness vs time since onset of symptoms (TOST)
Let $\De... | Python Code:
import itertools
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
import pandas as pd
from collections import namedtuple
from enum import Enum, IntEnum
from dataclasses import dataclass
import matplotlib.cm as cm
import sklearn
from sklearn import metrics
# Configure plot style sheet
p... |
11,282 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.algo - Réflexions autour du voyage de commerce (TSP)
Le problème du voyageur de commerce consiste à trouver le plus court chemin passant par toutes les villes. On parle aussi de circuit h... | Python Code:
%matplotlib inline
import random
n = 30
x = [ random.random() for _ in range(n) ]
y = [ random.random() for _ in range(n) ]
import matplotlib.pyplot as plt
plt.plot(x,y,"o")
Explanation: 2A.algo - Réflexions autour du voyage de commerce (TSP)
Le problème du voyageur de commerce consiste à trouver le plus c... |
11,283 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
=================================
Decoding sensor space data (MVPA)
=================================
Decoding, a.k.a MVPA or supervised machine learning, is applied to MEG
data in sensor sp... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator,
... |
11,284 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Contact Trajectories
Sometimes you're interested in how contacts evolve in a trajectory, frame-by-frame. Contact Map Explorer provides the ContactTrajectory class for this purpose.
We'll loo... | Python Code:
from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from contact_map import ContactTrajectory, RollingContactFrequency
import mdtraj as md
traj = md.load("data/gsk3b_example.h5")
print(traj) # to see number of frames; size of system
Explanation: Cont... |
11,285 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Open your dataset up using pandas in a Jupyter notebook
Step1: 3. Do a .columns to get a feel for your data
Step2: 4. Do a .head() to get a feel for your data
Step3: 4. Write down 12 q... | Python Code:
df
Explanation: 1. Open your dataset up using pandas in a Jupyter notebook
End of explanation
df.columns
Explanation: 3. Do a .columns to get a feel for your data
End of explanation
df.head()
Explanation: 4. Do a .head() to get a feel for your data
End of explanation
df['Agency'].value_counts()
print("NASA... |
11,286 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: Let's get started with some basic imports
Step2: And then we'll build a synthetic "dataset" and initialize a new bundle with those data
Step3: solver_times parameter and o... | Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: Advanced: solver_times
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
import phoebe
import numpy as np
import matplotlib.pyplot a... |
11,287 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Oracle and Python with oracledb
This is an example of how to query Oracle from Python
Setup and prerequisites
This is how you can setup an Oracle instance for testing using a docker image fo... | Python Code:
# connect to Oracle using oracledb
# !pip install oracledb
import oracledb
db_user = 'scott'
db_connect_string = 'localhost:1521/XEPDB1'
db_pass = 'tiger'
# To avoid storig connection passwords use getpas or db_config
# db_connect_string = 'dbserver:1521/orcl.mydomain.com'
# import getpass
# db_pass = getp... |
11,288 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--
29/10
Archivos de texto.
Operaciones con cadenas de caracteres.
String formaters.
CLASE DE LABORATORIO
(Gonzalo)
-->
Manejo de strings
Con los strings podemos hacer muchas operaciones... | Python Code:
cadena_caracteres = "Hola mundo"
print dir(cadena_caracteres)
Explanation: <!--
29/10
Archivos de texto.
Operaciones con cadenas de caracteres.
String formaters.
CLASE DE LABORATORIO
(Gonzalo)
-->
Manejo de strings
Con los strings podemos hacer muchas operaciones:
End of explanation
print 'Hola mundo'
pr... |
11,289 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Student Names
Learning Goals (Why are we asking you to do this?)
Core Assignment
Step1: Experimenting with this simulation
A Forest With Thunderbolt & Lightning, Very Very Frightening... | Python Code:
# Some setup to load outside web page elements
from IPython.display import IFrame
# When you execute this cell, it'll reset the simulationb
IFrame("http://ncase.me/simulating/model?local=forest/0_growth&play=0&edit=1", width=800, height=400)
Explanation: Title
Student Names
Learning Goals (Why are we askin... |
11,290 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to OnSSET Jupyter Interface¶
This page will guide you through the a simplified version of the OnSSET code, as well as the various parameters that can be set to generate any scenario ... | Python Code:
country = 'Malawi'
#Dependencies
from IPython.display import display, Markdown, HTML
import seaborn as sns
import matplotlib.pylab as plt
import folium
import branca.colormap as cm
import json
%matplotlib inline
%run onsset.py
Explanation: Welcome to OnSSET Jupyter Interface¶
This page will guide you throu... |
11,291 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Migrating from Spark to BigQuery via Dataproc -- Part 4
Part 1
Step1: Load data into BigQuery
Step2: BigQuery queries
We can replace much of the initial exploratory code by SQL statements.... | Python Code:
# Catch-up cell. Run if you did not do previous notebooks of this sequence
!wget http://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz
BUCKET='cloud-training-demos-ml' # CHANGE
!gsutil cp kdd* gs://$BUCKET/
Explanation: Migrating from Spark to BigQuery via Dataproc -- Part 4
Part 1: The orig... |
11,292 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Minimal Plugin setup
Plugins are dedicated analyzer to check the tags of one object at the time.
For explanation purpose only, we just here make a plugin that report fountains, it is not loo... | Python Code:
%cd "/opt/osmose-backend/"
Explanation: Minimal Plugin setup
Plugins are dedicated analyzer to check the tags of one object at the time.
For explanation purpose only, we just here make a plugin that report fountains, it is not looking for issue in the data.
End of explanation
from modules.OsmoseTranslation... |
11,293 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Forecasting, updating datasets, and the "news"
In this notebook, we describe how to use Statsmodels to compute the impacts of updated or revised datasets on out-of-sample forecasts or in-sam... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
macrodata = sm.datasets.macrodata.load_pandas().data
macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')
Explanation: Forecasting, updating datasets, and the "news"
In this not... |
11,294 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have a numpy array of different numpy arrays and I want to make a deep copy of the arrays. I found out the following: | Problem:
import numpy as np
pairs = [(2, 3), (3, 4), (4, 5)]
array_of_arrays = np.array([np.arange(a*b).reshape(a,b) for (a, b) in pairs])
import copy
result = copy.deepcopy(array_of_arrays) |
11,295 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro to ConPy
Step1: Loading the simulation data
If the code in the above cell ran without any errors, we're good to go. Let's load the stimulation data. It is stored as an MNE-Python Epoc... | Python Code:
# Don't worry about warnings in this exercise, as they can be distracting.
import warnings
warnings.simplefilter('ignore')
# Import the required Python modules
import mne
import conpy
import surfer
# Import and configure the 3D graphics backend
from mayavi import mlab
mlab.init_notebook('png')
# Tell MNE-P... |
11,296 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Run mcode on the adjacency list for your toy graph, with vwp=0.8
Load the Krogan et al. network edge-list data as a Pandas data frame
Step1: Make an igraph graph and print its summary
Step2... | Python Code:
edge_list =
Explanation: Run mcode on the adjacency list for your toy graph, with vwp=0.8
Load the Krogan et al. network edge-list data as a Pandas data frame
End of explanation
krogan_graph =
krogan_graph.summary()
Explanation: Make an igraph graph and print its summary
End of explanation
res =
Explana... |
11,297 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Unsupervised Anomaly Detection
Anomaly detection detects data points in data that does not fit well with the rest of data. In this notebook we demonstrate how to do anomaly detection for 1-D... | Python Code:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df_1932 = pd.read_csv("m_1932.csv", header=None, usecols=[1,2,3], names=["time_step", "cpu_usage","mem_usage"])
Explanation: Unsupervised Anomaly Detection
Anomaly detection detects data points in data that... |
11,298 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SpectralAnalysis showcase
I've written a library for regular plotting and curve fitting that is much, much nicer than the FittingRoutines module. In this notebook I'll be showing off (I gues... | Python Code:
testmodel = Model("Test") # set up a test model with linear fit
Explanation: SpectralAnalysis showcase
I've written a library for regular plotting and curve fitting that is much, much nicer than the FittingRoutines module. In this notebook I'll be showing off (I guess in a README-esque way) of how all... |
11,299 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Whiskey Data
This data set contains data on a small number of whiskies
Step1: Summaries
Shown below are the following charts
Step2: Some Analysis
Here we use the sci-kit decision tree regr... | Python Code:
import pandas as pd
from numpy import log, abs, sign, sqrt
import ibmcognitive
ibmcognitive.brunel.set_brunel_service_url("http://localhost:8080/BrunelServices")
data = pd.read_csv("data/whiskey.csv")
print('Data on whiskies:', ', '.join(data.columns))
Explanation: Whiskey Data
This data set contains data ... |
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